Cooperative Modulation Recognition Method based on Multi-Type Feature Parameters and Improved DS Evidence Theory

2013 ◽  
Vol 8 (11) ◽  
pp. 259-267
Author(s):  
Xin Yanshuang ◽  
Zhu Qi
Keyword(s):  
Evidence Theory ◽  
Modulation Recognition ◽  
Recognition Method ◽  
Feature Parameters

Research on Modulation Recognition Algorithm of Digital Communication Signal Based on Wavelet Denoising

2014 ◽  
Vol 608-609 ◽  
pp. 459-467 ◽  
Author(s):  
Xiao Yu Gu
Keyword(s):  
Pattern Recognition ◽  
Feature Extraction ◽  
Decision Theory ◽  
Recognition Rate ◽  
Recognition Algorithm ◽  
Modulation Recognition ◽  
The Core ◽  
Feature Parameters ◽  
Communication Signal ◽  
Modulation Signal

The paper researches a recognition algorithm of modulation signal and modulation modes. The modulation modes to be recognized include 2ASK, 2FSK, 2PSK, 4ASK, 4FSK and 4PSK modulation. There are two methods recognizing modulation modes of digital signal, method based on decision theory and pattern-recognition method based on feature extraction. The method based on decision theory is not suitable for recognition with multiple modulation modes. The core of pattern recognition based on feature extraction is selection of feature parameters. So the paper uses the feature parameters with simple calculation, easy to be implemented and high recognition rate as the core. The extraction of feature parameters is based on instant feature of modulation signal after Hilbert transformation.

scholarly journals Deep Learning-Based Automatic Modulation Recognition Method in the Presence of Phase Offset

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 42841-42847 ◽  
Author(s):  
Jie Shi ◽  
Sheng Hong ◽  
Changxin Cai ◽  
Yu Wang ◽  
Hao Huang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Modulation Recognition ◽  
Recognition Method ◽  
Phase Offset ◽  
Automatic Modulation Recognition

Human Posture Recognition Based on DAG-SVMS

2014 ◽  
Vol 1042 ◽  
pp. 117-120 ◽  
Author(s):  
Shuang Mei Wang ◽  
Yi Gao ◽  
Li Luo
Keyword(s):  
Feature Extraction ◽  
Moving Objects ◽  
Feature Vector ◽  
Moving Object ◽  
Recognition Method ◽  
Human Posture ◽  
Feature Parameters ◽  
Posture Recognition

A posture feature extraction and recognition method in monitoring environment is proposed in this paper which can recognize human shapes and analyze human postures. First contours of moving objects are extracted from two frames of a consecutive monitoring video. Then feature parameters are calculated from boundary contours to construct feature vector. In order to classify moving object and human and analyze postures, a DAG-SVMS is constructed by training 100 sample images. Results demonstrate the validity of this method.

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